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    "# Lecture One\n",
    "## Course Overview\n",
    "- Instructor Philosophy\n",
    "> Embrace diversity     \n",
    "> Seek an optimal pace       \n",
    "> Communicate early and often          \n",
    "> Success is not a grade        \n",
    "\n",
    "- Content Philosophy\n",
    "> Application-based approach         \n",
    "> Balance depth with breadth          \n",
    "> Modified based on experience            \n",
    "> Course project     \n",
    "\n",
    "- How to Succeed\n",
    "> Effort not prior knowledge            \n",
    "> Ask questions           \n",
    "> Help your classmates           \n",
    "> Be patient with yourself            \n",
    "\n",
    "- Typical Class\n",
    "> Discussion of pre-work          \n",
    "> Lecture          \n",
    "> Code walk-throughs      \n",
    "> In-class exercises             \n",
    "> Homework assigned        \n",
    "\n",
    "## Intro to Data Science\n",
    "### 1.What Is A Data Scientist?\u000b  \n",
    "- Data Scientist Type A (for Analysis):       \n",
    "> Primarily concerned with making sense of data or working with it in a fairly static way.       \n",
    "> Similar to a statistician, but knows all the practical details of working with data that aren't taught in statistics: data cleaning, dealing with large data sets, visualization, domain knowledge, etc.\n",
    "\n",
    "- Data Scientist Type B (for Building):\n",
    "> Some statistical background, but strong coder or software engineer.     \n",
    "> Primarily concerned with using data “in production”: building models which interact with users (by giving recommendations, for example).\n",
    "\n",
    "- Hadley Wickham’s **advice** for becoming a data scientist:\n",
    "> **Statistical knowledge**         \n",
    "> **Programming skills**\n",
    "\n",
    "### 2.Data Science Workflow\n"
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